WO2022048196A1 - 一种监测工业生产指数的方法及装置 - Google Patents

一种监测工业生产指数的方法及装置 Download PDF

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Publication number
WO2022048196A1
WO2022048196A1 PCT/CN2021/095288 CN2021095288W WO2022048196A1 WO 2022048196 A1 WO2022048196 A1 WO 2022048196A1 CN 2021095288 W CN2021095288 W CN 2021095288W WO 2022048196 A1 WO2022048196 A1 WO 2022048196A1
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Prior art keywords
monitored
preset
data
industrial area
industrial
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PCT/CN2021/095288
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English (en)
French (fr)
Inventor
汪飙
侯鑫
邹冲
朱超杰
吴海山
殷磊
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深圳前海微众银行股份有限公司
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Publication of WO2022048196A1 publication Critical patent/WO2022048196A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/13—Satellite images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/28—Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/56—Extraction of image or video features relating to colour
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/194—Terrestrial scenes using hyperspectral data, i.e. more or other wavelengths than RGB

Definitions

  • the invention relates to the field of financial technology (Fintech), in particular to a method and device for monitoring an industrial production index.
  • the present invention provides a method and device for monitoring an industrial production index, which can solve the problems in the prior art that the cost of large-scale factory monitoring is high and the data processing process is complicated and difficult to achieve.
  • the present invention provides a method for monitoring an industrial production index, comprising:
  • the determining the binarization map of the image of the industrial area to be monitored includes:
  • the image processing efficiency can be improved, and the efficiency of monitoring the industrial production index can be improved.
  • performing color conversion on the image of the industrial area to be monitored to obtain color space data of the image of the industrial area to be monitored including:
  • HSV Hue Saturation Value, hue-saturation-lightness
  • the determining the coordinate set of the preset shape range of the industrial area to be monitored includes:
  • the area of the preset shape range is expanded to obtain a coordinate set of the preset shape range of the industrial area to be monitored.
  • the coordinates in the coordinate set are latitude and longitude coordinates
  • the determining, according to the coordinate set, the remote sensing satellite multispectral data in the preset period corresponding to the coordinate set includes:
  • the remote sensing satellite multispectral data corresponding to the latitude and longitude coordinates in the coordinate set within a preset period is determined from the remote sensing satellite database.
  • obtaining the image of the industrial area to be monitored based on the data of the multiple preset bands includes:
  • the normalized data of the multiple preset bands are combined to obtain an image of the industrial area to be monitored.
  • performing normalization processing on the data of the multiple preset bands includes:
  • the ratio of the product of the first difference and the preset threshold to the second difference is determined as the normalized data of the preset band;
  • the first difference is the difference between the data value of the preset band and the minimum value of the preset band;
  • the second difference is the difference between the maximum value and the minimum value of the preset band difference.
  • the method further includes:
  • the industrial production index of the industrial area to be monitored in the preset period is analyzed to determine the industrial production situation of the industrial area to be monitored.
  • an embodiment of the present invention provides a device for monitoring an industrial production index, including:
  • a determining unit configured to determine a coordinate set of a preset shape range of the industrial area to be monitored
  • a processing unit configured to determine, according to the coordinate set, the remote sensing satellite multispectral data in a preset period corresponding to the coordinate set; and extract data of multiple preset bands in the remote sensing satellite multispectral data within the preset period , based on the data of the plurality of preset bands, obtain the image of the industrial area to be monitored; and determine the binarization map of the image of the industrial area to be monitored; count the to-be-monitored images in the preset period The number of pixel points in the binarized image of the image of the industrial area whose pixels meet the preset pixel condition is used to obtain the industrial production index of the industrial area to be monitored in the preset period.
  • processing unit is specifically used for:
  • processing unit is specifically used for:
  • the determining unit is specifically used for:
  • the area of the preset shape range is expanded to obtain a coordinate set of the preset shape range of the industrial area to be monitored.
  • the coordinates in the coordinate set are latitude and longitude coordinates
  • the processing unit is specifically used for:
  • the remote sensing satellite multispectral data corresponding to the latitude and longitude coordinates in the coordinate set within a preset period is determined from the remote sensing satellite database.
  • processing unit is specifically used for:
  • the normalized data of the multiple preset bands are combined to obtain an image of the industrial area to be monitored.
  • processing unit is specifically used for:
  • the ratio of the product of the first difference and the preset threshold to the second difference is determined as the normalized data of the preset band;
  • the first difference is the difference between the data value of the preset band and the minimum value of the preset band;
  • the second difference is the difference between the maximum value and the minimum value of the preset band difference.
  • processing unit is also used for:
  • the present invention provides a computing device, comprising:
  • the processor is configured to call the computer program stored in the memory, and execute the method described in the first aspect according to the obtained program.
  • the present invention provides a computer-readable storage medium, where the computer-readable storage medium stores a computer-executable program, and the computer-executable program is used to cause a computer to execute the method described in the first aspect.
  • an embodiment of the present invention further provides a computer program product including instructions, the computer program product includes a computer program stored on a computer-readable storage medium, the computer program includes program instructions, and when the program instructions When executed by a computer, the computer is caused to execute the method described in the first aspect.
  • the remote sensing data corresponding to the coordinate set is obtained, and then the data of the preset frequency band is extracted from the remote sensing data and processed to obtain the to-be-monitored data.
  • the image of the industrial area is binarized, and the number of pixels in the preset pixel condition of the pixel is counted, so that the monitoring of the industrial production index can be realized. Since the installation of sensor equipment is not involved in the monitoring process, the input cost of industrial production monitoring can be reduced.
  • the complexity of building a prediction model in the data processing process is lower than that in the prior art, and the monitoring of the industrial production index in any region and any range can be realized.
  • FIG. 1 is a schematic diagram of a system architecture according to an embodiment of the present invention.
  • FIG. 2 is a schematic flowchart of a method for monitoring an industrial production index provided by an embodiment of the present invention
  • FIG. 3 is a schematic diagram of band data of a kind of remote sensing data provided by an embodiment of the present invention.
  • FIG. 4 is a schematic diagram of an RGB image provided by an embodiment of the present invention.
  • FIG. 5 is a schematic diagram of a binarized image provided by an embodiment of the present invention.
  • FIG. 6 is a schematic diagram of an industrial production index provided by an embodiment of the present invention.
  • FIG. 7 is a schematic structural diagram of a device for monitoring an industrial production index provided by an embodiment of the present invention.
  • FIG. 8 is a schematic structural diagram of a computing device according to an embodiment of the present invention.
  • FIG. 2 exemplarily shows a flow of a method for monitoring an industrial production index provided by an embodiment of the present invention, and the flow may be executed by a device for monitoring an industrial production index.
  • Step 201 Determine the coordinate set of the preset shape range of the industrial area to be monitored.
  • the coordinates in the coordinate set may be longitude and latitude coordinates, or may be map coordinates.
  • the coordinates of the center point are the latitude and longitude coordinates of the center point
  • the set of coordinates is the set of latitude and longitude coordinates.
  • the latitude and longitude coordinates will be used as an example for description below.
  • the preset shape range may be a rectangular frame range, a circular frame range, a trapezoidal frame range, etc., which is not specifically limited in this embodiment of the present invention.
  • the size of the range can be set empirically.
  • the coordinate set in this embodiment of the present invention may be the coordinates of all points within the preset shape range, or may be coordinates that can represent the preset shape range, or the minimum horizontal and vertical coordinates within the preset shape range, such as the minimum longitude Coordinates, Min Dimension Coordinates, Max Longitude Coordinates, and Max Dimension Coordinates.
  • the industrial area to be monitored must be determined, and N industrial areas to be monitored can be obtained by means of network search and collection.
  • the latitude and longitude coordinates (Lati, Loni) (i ⁇ [1,N]) of the center point of the N industrial areas to be monitored are recorded.
  • BOXi [minimum longitude, minimum latitude, maximum longitude, maximum latitude] (i ⁇ [ 1,N]).
  • BOXi [minimum longitude, minimum latitude, maximum longitude, maximum latitude] (i ⁇ [ 1,N]).
  • BOXi [Loni-Lon_bias,Lati-Lat_bias,Loni+Lon_bias,Lati+ Lat_bias].
  • Step 202 determining, according to the coordinate set, the remote sensing satellite multispectral data in a preset period corresponding to the coordinate set;
  • the remote sensing satellite multispectral data corresponding to the coordinate set can be determined, and the preset period can be set according to experience, or set according to monitoring tasks.
  • the plurality of preset wavelength bands may be set according to experience, for example, the multiple preset wavelength bands may include at least short-wave infrared wavelength bands.
  • the remote sensing satellite multispectral data can be obtained in the remote sensing satellite database, that is, based on the latitude and longitude coordinates in the above coordinate set, the remote sensing satellite multispectral data corresponding to the latitude and longitude coordinates in the coordinate set within the preset period is obtained from the remote sensing satellite multispectral data. data.
  • Remote sensing satellite multispectral data can include multiple bands, as shown in Figure 3, which can include red band, green band, blue band, near-infrared band, short-wave infrared band, etc.
  • each band of remote sensing satellite multispectral data can be shown in Figure 3, where Band 11 and Band 12 are two short-wave infrared bands. In this embodiment of the present invention, at least Band will be included 12 multiple bands as an example for data extraction and processing.
  • Step 203 Extract the data of multiple preset bands in the multispectral data of the remote sensing satellites in the preset period, obtain the image of the industrial area to be monitored based on the data of the multiple preset bands, and determine the image of the industrial area to be monitored. The binarized image of the image of the industrial area to be monitored.
  • the data of multiple preset bands can be normalized, and then the normalized data of multiple preset bands can be combined to obtain an image of the industrial area to be monitored.
  • the data of the multiple preset bands is normalized into a preset range by means of percentage truncation.
  • the ratio of the product of the first difference and the preset threshold to the second difference may be determined as the normalized data of the preset band.
  • the first difference is the difference between the data value of the preset band and the minimum value of the preset band.
  • the second difference is the difference between the maximum value and the minimum value of the preset band.
  • the maximum value and the minimum value of the preset band are the maximum value and the minimum value in the fluctuation range of the preset band.
  • the preset threshold can be set empirically.
  • the data of Band 12, Band 8A, and Band 4 in Figure 3 can be extracted respectively, and the data of Band 12, Band 12, Band The data of 8A and Band 4 are normalized to 0-255 (preset range) respectively.
  • RAWband4_s (RAWband4 - RAWband4_min)* 255 / (RAWband4_max - RAWband4_min);
  • RAWband4_min and RAWband4_max are the minimum value and the maximum value of the fluctuation range of the band of RAWband4, respectively.
  • the normalized values RAWband4_s, RAWband8a_s, RAWband12_s of the three bands of Band 4, Band 8A, and Band 12 can be obtained by calculating in turn.
  • the normalized data of multiple preset bands can be merged.
  • the way of merging is to superimpose the data of multiple preset bands. , so that a color RGB image can be obtained, that is, the image of the industrial area to be monitored.
  • the image of the industrial area to be monitored is converted to obtain its corresponding binarized image.
  • it is necessary to perform color conversion on the image of the industrial area to be monitored to obtain color space data of the image of the industrial area to be monitored. It may be to perform color conversion from the RGB color space to the HSV color space for the image of the industrial area to be monitored, to obtain the HVS color space data of the image of the industrial area to be monitored.
  • color extraction is performed on the color space data according to the set extraction range, and the pixels of the pixels that meet the set extraction range are set to the first value, and the pixels of the pixels that do not meet the set extraction range are set to the second value.
  • the set extraction range may include a hue extraction range, a saturation extraction range, and a lightness extraction range.
  • the hue extraction range, saturation extraction range, and lightness extraction range can be set empirically.
  • the first value and the second setting can be set based on experience. For example, the first value may be 1 or 255, and the second value may be 0. Or the first value is 0 and the second value is 1 or 255.
  • the normalized values RAWband4_s, RAWband8a_s, and RAWband12_s obtained in the above-mentioned embodiments may be subjected to a band combining operation.
  • the order [RAWband12_s, RAWband8a_s, RAWband4_s] compose the RGB image of this steel plant, as shown in Figure 4.
  • the location circled by the black oval in Figure 4 represents the high temperature heating area of the steel plant.
  • the color conversion of the RGB color space to the HSV color space is performed on the RGB image shown in FIG. 4 to obtain the HSV color space data of the RGB image.
  • the HSV color space data is filtered by the above-mentioned set extraction range.
  • the data elements that conform to the above set extraction range are set to RGB[255,255,255], and the data elements that do not conform to the above set range are set to RGB[0,0,0].
  • the black and white binarization map corresponding to the above RGB image is obtained (black corresponds to [0,0,0], and white corresponds to [255,255,255]).
  • the binarization map corresponding to FIG. 4 may be shown in FIG. 5 .
  • Step 204 Count the number of pixels in the binarized image of the image of the industrial area to be monitored that meet the preset pixel condition within the preset period, and obtain the number of pixels of the industrial area to be monitored within the preset period.
  • Industrial production index Count the number of pixels in the binarized image of the image of the industrial area to be monitored that meet the preset pixel condition within the preset period, and obtain the number of pixels of the industrial area to be monitored within the preset period.
  • the preset pixel condition may be set according to experience, for example, the pixel may be larger than the preset pixel threshold, the pixel is located within the preset pixel threshold range, and so on.
  • SWIR-SMI short-wave (length) infrared (band)- Stochastic Momentum Index, SWIR-Stochastic Momentum.
  • the SWIR-SMI can reflect the overall situation of industrial production in this industrial area. By analyzing the industrial production index of the industrial area to be monitored in a preset period, the industrial production situation of the industrial area to be monitored can be determined.
  • the short-wave infrared images of the main steel plants in the entire steel industry in a preset period are obtained, the SWIR-SMI of each iron and steel plant in the preset period is extracted, and the SWIR-SMI of each iron and steel plant is analyzed, The industrial production situation of the entire steel industry can be obtained.
  • the solid black line is the SWIR-SMI extracted by date.
  • the black dotted line is the industrial growth value of steel, which can also be called the steel production index.
  • the correlation analysis between the steel production index and SWIR-SMI is carried out. If the correlation coefficient R > the set value (such as 0.7), there is a strong correlation, which proves that SWIR-SMI can reflect the steel production index of the steel industry.
  • the satellite remote sensing technology by applying the satellite remote sensing technology, it is theoretically possible to monitor the production activities of any industrial area in the world within a certain period.
  • the application of multi-spectral data processing and analysis technology can analyze and monitor the production activities of the industrial zone from different characteristic levels.
  • the image processing technology is applied, and the white point extraction algorithm of the threshold can be used to extract the sum of the white point pixel value in the effective industrial production area.
  • the embodiment of the present invention shows that by determining the coordinate set of the preset shape range of the industrial area to be monitored, the multispectral data of the remote sensing satellites in the preset period corresponding to the coordinate set is determined according to the coordinate set, and the remote sensing satellite multispectral data in the preset period is extracted.
  • the data of multiple preset bands in the spectral data based on the data of multiple preset bands, obtain the image of the industrial area to be monitored, and determine the binarized image of the image of the industrial area to be monitored, and count the information in the preset period.
  • the number of pixel points whose pixels meet the preset pixel condition in the binarized image of the image of the industrial area to be monitored is used to obtain the industrial production index of the industrial area to be monitored within the preset period.
  • the remote sensing data corresponding to the coordinate set is obtained, and then the data of the preset frequency band is extracted from the remote sensing data and processed to obtain the image of the industrial area to be monitored.
  • the monitoring of the industrial production index can be realized. Since the installation of sensor equipment is not involved in the monitoring process, the input cost of industrial production monitoring can be reduced.
  • the complexity of building a prediction model in the data processing process is lower than that in the prior art, and the monitoring of the industrial production index in any region and any range can be realized.
  • FIG. 1 is a system architecture provided by an embodiment of the present invention.
  • the system architecture may be a server 100 , including a processor 110 , a communication interface 120 and a memory 130 .
  • the communication interface 120 is used for communicating with the terminal device, sending and receiving information transmitted by the terminal device, and realizing communication.
  • the processor 110 is the control center of the server 100, using various interfaces and lines to connect various parts of the entire server 100, by running or executing the software programs/or modules stored in the memory 130, and calling the data stored in the memory 130, Various functions of the server 100 are executed and data is processed.
  • processor 110 may include one or more processing units.
  • the memory 130 may be used to store software programs and modules, and the processor 110 executes various functional applications and data processing by running the software programs and modules stored in the memory 130 .
  • the memory 130 may mainly include a stored program area and a stored data area, wherein the stored program area may store an operating system, an application program required for at least one function, and the like; the stored data area may store data created according to business processing, and the like. Additionally, memory 130 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
  • FIG. 1 the structure shown in FIG. 1 above is only an example, which is not limited in this embodiment of the present invention.
  • FIG. 2 exemplarily shows a flow of a method for monitoring an industrial production index provided by an embodiment of the present invention, and the flow may be executed by a device for monitoring an industrial production index.
  • Step 201 Determine the coordinate set of the preset shape range of the industrial area to be monitored.
  • the coordinates in the coordinate set may be longitude and latitude coordinates, or may be map coordinates.
  • the coordinates of the center point are the latitude and longitude coordinates of the center point
  • the set of coordinates is the set of latitude and longitude coordinates.
  • the latitude and longitude coordinates will be used as an example for description below.
  • the preset shape range may be a rectangular frame range, a circular frame range, a trapezoidal frame range, etc., which is not specifically limited in this embodiment of the present invention.
  • the size of the range can be set empirically.
  • the coordinate set in this embodiment of the present invention may be the coordinates of all points within the preset shape range, or may be coordinates that can represent the preset shape range, or the minimum horizontal and vertical coordinates within the preset shape range, such as the minimum longitude Coordinates, Min Dimension Coordinates, Max Longitude Coordinates, and Max Dimension Coordinates.
  • the industrial area to be monitored must be determined, and N industrial areas to be monitored can be obtained by means of network search and collection.
  • the latitude and longitude coordinates (Lati, Loni) (i ⁇ [1,N]) of the center point of the N industrial areas to be monitored are recorded.
  • BOXi [minimum longitude, minimum latitude, maximum longitude, maximum latitude] (i ⁇ [ 1,N]).
  • BOXi [minimum longitude, minimum latitude, maximum longitude, maximum latitude] (i ⁇ [ 1,N]).
  • BOXi [Loni-Lon_bias,Lati-Lat_bias,Loni+Lon_bias,Lati+ Lat_bias].
  • Step 202 determining, according to the coordinate set, the remote sensing satellite multispectral data in a preset period corresponding to the coordinate set;
  • the remote sensing satellite multispectral data corresponding to the coordinate set can be determined, and the preset period can be set according to experience, or set according to monitoring tasks.
  • the plurality of preset wavelength bands may be set according to experience, for example, the multiple preset wavelength bands may include at least short-wave infrared wavelength bands.
  • the remote sensing satellite multispectral data can be obtained in the remote sensing satellite database, that is, based on the latitude and longitude coordinates in the above coordinate set, the remote sensing satellite multispectral data corresponding to the latitude and longitude coordinates in the coordinate set within the preset period is obtained from the remote sensing satellite multispectral data. data.
  • Remote sensing satellite multispectral data can include multiple bands, as shown in Figure 3, which can include red band, green band, blue band, near-infrared band, short-wave infrared band, etc.
  • each band of remote sensing satellite multispectral data can be shown in Figure 3, where Band 11 and Band 12 are two short-wave infrared bands. In this embodiment of the present invention, at least Band will be included 12 multiple bands as an example for data extraction and processing.
  • Step 203 Extract the data of multiple preset bands in the multispectral data of the remote sensing satellites in the preset period, obtain the image of the industrial area to be monitored based on the data of the multiple preset bands, and determine the image of the industrial area to be monitored. The binarized image of the image of the industrial area to be monitored.
  • the data of multiple preset bands can be normalized, and then the normalized data of multiple preset bands can be combined to obtain an image of the industrial area to be monitored.
  • the data of the multiple preset bands is normalized into a preset range by means of percentage truncation.
  • the ratio of the product of the first difference and the preset threshold to the second difference may be determined as the normalized data of the preset band.
  • the first difference is the difference between the data value of the preset band and the minimum value of the preset band.
  • the second difference is the difference between the maximum value and the minimum value of the preset band.
  • the maximum value and the minimum value of the preset band are the maximum value and the minimum value in the fluctuation range of the preset band.
  • the preset threshold can be set empirically.
  • the data of Band 12, Band 8A, and Band 4 in Figure 3 can be extracted respectively, and the data of Band 12, Band 12, Band The data of 8A and Band 4 are normalized to 0-255 (preset range) respectively.
  • RAWband4_s (RAWband4 - RAWband4_min)* 255 / (RAWband4_max - RAWband4_min);
  • RAWband4_min and RAWband4_max are the minimum value and the maximum value of the fluctuation range of the band of RAWband4, respectively.
  • the normalized values RAWband4_s, RAWband8a_s, RAWband12_s of the three bands of Band 4, Band 8A, and Band 12 can be obtained by calculating in turn.
  • the normalized data of multiple preset bands can be merged.
  • the way of merging is to superimpose the data of multiple preset bands. , so that a color RGB image can be obtained, that is, the image of the industrial area to be monitored.
  • the image of the industrial area to be monitored is converted to obtain its corresponding binarized image.
  • it is necessary to perform color conversion on the image of the industrial area to be monitored to obtain color space data of the image of the industrial area to be monitored. It may be to perform color conversion from the RGB color space to the HSV color space for the image of the industrial area to be monitored, to obtain the HVS color space data of the image of the industrial area to be monitored.
  • color extraction is performed on the color space data according to the set extraction range, and the pixels of the pixels that meet the set extraction range are set to the first value, and the pixels of the pixels that do not meet the set extraction range are set to the second value.
  • the set extraction range may include a hue extraction range, a saturation extraction range, and a lightness extraction range.
  • the hue extraction range, saturation extraction range, and lightness extraction range can be set empirically.
  • the first value and the second setting can be set based on experience. For example, the first value may be 1 or 255, and the second value may be 0. Or the first value is 0 and the second value is 1 or 255.
  • the normalized values RAWband4_s, RAWband8a_s, and RAWband12_s obtained in the above-mentioned embodiments may be subjected to a band combining operation.
  • the order [RAWband12_s, RAWband8a_s, RAWband4_s] compose the RGB image of this steel plant, as shown in Figure 4.
  • the location circled by the black oval in Figure 4 represents the high temperature heating area of the steel plant.
  • the color conversion of the RGB color space to the HSV color space is performed on the RGB image shown in FIG. 4 to obtain the HSV color space data of the RGB image.
  • the HSV color space data is filtered by the above-mentioned set extraction range.
  • the data elements that conform to the above set extraction range are set to RGB[255,255,255], and the data elements that do not conform to the above set range are set to RGB[0,0,0].
  • the black and white binarization map corresponding to the above RGB image is obtained (black corresponds to [0,0,0], and white corresponds to [255,255,255]).
  • the binarization map corresponding to FIG. 4 may be shown in FIG. 5 .
  • Step 204 Count the number of pixels in the binarized image of the image of the industrial area to be monitored that meet the preset pixel condition within the preset period, and obtain the number of pixels of the industrial area to be monitored within the preset period.
  • Industrial production index Count the number of pixels in the binarized image of the image of the industrial area to be monitored that meet the preset pixel condition within the preset period, and obtain the number of pixels of the industrial area to be monitored within the preset period.
  • the preset pixel condition may be set based on experience, for example, the pixel may be larger than the preset pixel threshold, the pixel is located within the preset pixel threshold range, and so on.
  • SWIR-SMI short-wave (length) infrared (band)- Stochastic Momentum Index, SWIR-Stochastic Momentum.
  • the SWIR-SMI can reflect the overall situation of industrial production in this industrial area. By analyzing the industrial production index of the industrial area to be monitored in a preset period, the industrial production situation of the industrial area to be monitored can be determined.
  • the short-wave infrared images of the main steel plants in the entire steel industry in a preset period are obtained, the SWIR-SMI of each iron and steel plant in the preset period is extracted, and the SWIR-SMI of each iron and steel plant is analyzed, The industrial production situation of the entire steel industry can be obtained.
  • the solid black line is the SWIR-SMI extracted by date.
  • the black dotted line is the industrial growth value of steel, which can also be called the steel production index.
  • the correlation analysis between the steel production index and SWIR-SMI is carried out. If the correlation coefficient R > the set value (such as 0.7), there is a strong correlation, which proves that SWIR-SMI can reflect the steel production index of the steel industry.
  • the satellite remote sensing technology by applying the satellite remote sensing technology, it is theoretically possible to monitor the production activities of any industrial area in the world within a certain period.
  • the application of multi-spectral data processing and analysis technology can analyze and monitor the production activities of the industrial zone from different characteristic levels.
  • the image processing technology is applied, and the white point extraction algorithm of the threshold can be used to extract the sum of the white point pixel value in the effective industrial production area.
  • the embodiment of the present invention shows that by determining the coordinate set of the preset shape range of the industrial area to be monitored, the multispectral data of the remote sensing satellites in the preset period corresponding to the coordinate set is determined according to the coordinate set, and the remote sensing satellite multispectral data in the preset period is extracted.
  • the data of multiple preset bands in the spectral data based on the data of multiple preset bands, obtain the image of the industrial area to be monitored, and determine the binarized image of the image of the industrial area to be monitored, and count the information in the preset period.
  • the number of pixel points whose pixels meet the preset pixel condition in the binarized image of the image of the industrial area to be monitored is used to obtain the industrial production index of the industrial area to be monitored in the preset period.
  • the remote sensing data corresponding to the coordinate set is obtained, and then the data of the preset frequency band is extracted from the remote sensing data and processed to obtain the image of the industrial area to be monitored.
  • the monitoring of the industrial production index can be realized. Since the installation of sensor equipment is not involved in the monitoring process, the input cost of industrial production monitoring can be reduced.
  • the complexity of building a prediction model in the data processing process is lower than that in the prior art, and the monitoring of the industrial production index in any region and any range can be realized.
  • FIG. 7 exemplarily shows a schematic structural diagram of an apparatus for monitoring an industrial production index provided by an embodiment of the present invention, and the apparatus can execute a process for monitoring an industrial production index.
  • the device specifically includes:
  • a determination unit 701 configured to determine a coordinate set of a preset shape range of an industrial area to be monitored
  • the processing unit 702 is configured to determine, according to the coordinate set, the remote sensing satellite multispectral data in a preset period corresponding to the coordinate set; data, based on the data of the multiple preset bands, obtain the image of the industrial area to be monitored; and determine the binarization map of the image of the industrial area to be monitored; count the industrial area to be monitored in the preset period.
  • the number of pixel points whose pixels meet the preset pixel condition in the binarization map of the image of the monitored industrial area is used to obtain the industrial production index of the industrial area to be monitored in the preset period.
  • processing unit 702 is specifically configured to:
  • processing unit 702 is specifically configured to:
  • the determining unit 701 is specifically configured to:
  • the area of the preset shape range is expanded to obtain a coordinate set of the preset shape range of the industrial area to be monitored.
  • the coordinates in the coordinate set are latitude and longitude coordinates
  • the processing unit 702 is specifically configured to:
  • the remote sensing satellite multispectral data corresponding to the latitude and longitude coordinates in the coordinate set within a preset period is determined from the remote sensing satellite database.
  • processing unit 702 is specifically configured to:
  • the normalized data of the multiple preset bands are combined to obtain an image of the industrial area to be monitored.
  • processing unit 702 is specifically configured to:
  • the ratio of the product of the first difference and the preset threshold to the second difference is determined as the normalized data of the preset band;
  • the first difference is the difference between the data value of the preset band and the minimum value of the preset band;
  • the second difference is the difference between the maximum value and the minimum value of the preset band difference.
  • processing unit 702 is further configured to:
  • the present application further provides a computing device.
  • the computing device includes at least one processor 820, which is configured to implement the method shown in FIG. 2 provided by the embodiment of the present application. either method.
  • Computing device 800 may also include at least one memory 830 for storing program instructions and/or data.
  • Memory 830 is coupled to processor 820 .
  • the coupling in the embodiments of the present application is an indirect coupling or communication connection between devices, units or modules, which may be in electrical, mechanical or other forms, and is used for information exchange between devices, units or modules.
  • Processor 820 may cooperate with memory 830 .
  • Processor 820 may execute program instructions stored in memory 830 . At least one of the at least one memory may be included in the processor.
  • each step of the above-mentioned method can be completed by a hardware integrated logic circuit in a processor or an instruction in the form of software.
  • the steps of the methods disclosed in conjunction with the embodiments of the present application may be directly embodied as executed by a hardware processor, or executed by a combination of hardware and software modules in the processor.
  • the software modules may be located in random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers and other storage media mature in the art.
  • the storage medium is located in the memory, and the processor reads the information in the memory, and completes the steps of the above method in combination with its hardware. To avoid repetition, detailed description is omitted here.
  • the processor in this embodiment of the present application may be an integrated circuit chip, which has a signal processing capability.
  • each step of the above method embodiments may be completed by a hardware integrated logic circuit in a processor or an instruction in the form of software.
  • the above-mentioned processor can be a general-purpose processor, a digital signal processing circuit (digital signal processor, DSP), application specific integrated circuit (ASIC), field programmable gate array (field programmable gate array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • FPGA field programmable gate array
  • a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
  • the steps of the method disclosed in conjunction with the embodiments of the present application may be directly embodied as executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor.
  • the software modules may be located in random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers and other storage media mature in the art.
  • the storage medium is located in the memory, and the processor reads the information in the memory, and completes the steps of the above method in combination with its hardware.
  • the memory in this embodiment of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory.
  • the non-volatile memory can be read-only memory (read-only memory) memory, ROM), programmable read-only memory (programmable ROM, PROM), erasable programmable read-only memory (erasable PROM, EPROM), Electrically Erasable Programmable Read-Only Memory (electrically EPROM, EEPROM) or flash memory.
  • Volatile memory can be random access memory (random access memory, RAM), which is used as an external cache.
  • RAM static random access memory
  • DRAM dynamic random access memory
  • SDRAM synchronous DRAM
  • SDRAM double data rate synchronous dynamic random access memory
  • ESDRAM enhanced synchronous dynamic random access memory
  • SLDRAM synchronous link dynamic random access memory
  • direct rambus RAM direct rambus RAM
  • Computing device 800 may also include a communication interface 810 for communicating with other devices over a transmission medium so that means used in computing device 800 may communicate with other devices.
  • the communication interface may be a transceiver, a circuit, a bus, a module, or other types of communication interfaces.
  • the transceiver when the communication interface is a transceiver, the transceiver may include an independent receiver and an independent transmitter; it may also be a transceiver integrating a transceiver function, or an interface circuit.
  • Computing device 800 may also include communication line 840 .
  • the communication interface 810, the processor 820 and the memory 830 may be connected to each other through a communication line 840; the communication line 840 may be a peripheral component interconnection standard component interconnect, referred to as PCI) bus or extended industry standard architecture (extended industry standard architecture, referred to as EISA) bus and so on.
  • the communication line 840 can be divided into an address bus, a data bus, a control bus, and the like. For ease of presentation, only one thick line is used in FIG. 8, but it does not mean that there is only one bus or one type of bus.
  • an embodiment of the present invention provides a computing device, including:
  • the processor is used for calling the computer program stored in the memory, and executing the above-mentioned method for monitoring the industrial production index according to the obtained program.
  • an embodiment of the present invention provides a computer-readable storage medium, where the computer-readable storage medium stores a computer-executable program, and the computer-executable program is used to make a computer execute the above-mentioned monitoring of the industrial production index. method.
  • an embodiment of the present invention provides a computer program product, the computer program product includes a computer program stored on a computer-readable storage medium, the computer program includes program instructions, and when the program instructions are executed by a computer When executed, the computer is made to execute the above-mentioned method for monitoring the industrial production index.
  • embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
  • computer-usable storage media including, but not limited to, disk storage, CD-ROM, optical storage, etc.
  • These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions
  • An apparatus implements the functions specified in a flow or flows of the flowcharts and/or a block or blocks of the block diagrams.
  • These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in one or more of the flowcharts and/or one or more blocks of the block diagrams.

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Abstract

一种监测工业生产指数的方法及装置，该方法包括确定待监测工业区域的预设形状范围的坐标集合，根据坐标集合确定出坐标集合对应的预设周期内的遥感卫星多光谱数据，提取预设周期内的遥感卫星多光谱数据中多个预设波段的数据，基于多个预设波段的数据，得到待监测工业区域的图像，并确定出待监测工业区域的图像的二值化图，统计预设周期内所述待监测工业区域的图像的二值化图中像素符合预设像素条件的像素点的数量，得到预设周期内所述待监测工业区域的工业生产指数。通过基于遥感数据的工业生产指数的监测，在数据处理过程中相比现有技术中的构建预测模型方式复杂度要低，能够实现在任意地区的任意范围的工业生产指数的监测。

Description

一种监测工业生产指数的方法及装置 技术领域
本发明涉及金融科技（Fintech）领域，尤其涉及一种监测工业生产指数的方法及装置。
 背景技术
随着计算机技术的发展，越来越多的技术应用在金融领域，传统金融业正在逐步向金融科技转变，但由于金融行业的安全性、实时性要求，也对技术提出的更高的要求。在金融领域的数据监测技术中，监测工业生产指数是数据监测技术中一个重要的问题。
在工业生产活动监测的过程中，通常需要运用大量的传感器来对监测对象进行数据监测，但是这种方式只适合小规模的工厂，对于大规模的工厂需要的传感器数量比较大，需要增加大量的成本投入。此外在数据分析过程中，需要构建各种数据预测模型，对数据要求高且处理过程复杂，难以实现应用。
综上，目前亟需一种监测工业生产指数的方法，用以降低工业生产监测的难度。
 技术问题
本发明提供了一种监测工业生产指数的方法及装置，可以解决现有技术中存在对于大规模的工厂监测成本投入高且数据处理过程复杂难以实现的问题。
 技术解决方案
第一方面，本发明提供了一种监测工业生产指数的方法，包括：
确定待监测工业区域的预设形状范围的坐标集合；
根据所述坐标集合确定出所述坐标集合对应的预设周期内的遥感卫星多光谱数据；
提取所述预设周期内的遥感卫星多光谱数据中多个预设波段的数据，基于所述多个预设波段的数据，得到所述待监测工业区域的图像，并确定出所述待监测工业区域的图像的二值化图；
统计所述预设周期内所述待监测工业区域的图像的二值化图中像素符合预设像素条件的像素点的数量，得到所述预设周期内所述待监测工业区域的工业生产指数。
可选的，所述确定出所述待监测工业区域的图像的二值化图，包括：
将所述待监测工业区域的图像进行色彩转换，得到所述待监测工业区域的图像的色彩空间数据；
依据设定提取范围对所述色彩空间数据进行颜色提取，将符合所述设定提取范围的像素点的像素设置为第一数值，将不符合所述设定提取范围的像素点的像素设置为第二数值。
上述技术方案中，通过将待监测工业区域的图像转换为二值化图，可以提高图像处理效率，提高监控工业生产指数的效率。
可选的，所述将所述待监测工业区域的图像进行色彩转换，得到所述待监测工业区域的图像的色彩空间数据，包括：
对所述待监测工业区域的图像进行RGB（Red Green Blue，红-绿-蓝）颜色空间至HSV（Hue Saturation Value，色调-饱和度-明度）颜色空间的色彩转换，得到所述待监测工业区域的图像的HVS色彩空间数据。
可选的，所述确定待监测工业区域的预设形状范围的坐标集合，包括：
获取待监测工业区域以及所述待监测工业区域的中心点坐标；
以所述待监测工业区域的中心点坐标为原点，外扩预设形状范围的区域，得到所述待监测工业区域的预设形状范围的坐标集合。
可选的，所述坐标集合中的坐标为经纬度坐标；
所述根据所述坐标集合确定出所述坐标集合对应的预设周期内的遥感卫星多光谱数据，包括：
基于所述坐标集合中的经纬度坐标，从遥感卫星数据库中确定出预设周期内的所述坐标集合中的经纬度坐标对应的遥感卫星多光谱数据。
可选的，所述基于所述多个预设波段的数据，得到所述待监测工业区域的图像，包括：
对所述多个预设波段的数据进行归一化处理；
将归一化处理后的所述多个预设波段的数据进行合并，得到所述待监测工业区域的图像。
可选的，所述对所述多个预设波段的数据进行归一化处理，包括：
针对所述多个预设波段中的任一预设波段，将第一差值和预设阈值的乘积与第二差值的比值确定为归一化处理后的所述预设波段的数据；其中，所述第一差值为所述预设波段的数据的值与所述预设波段的最小值的差值；所述第二差值为所述预设波段的最大值和最小值的差值。
可选的，在得到所述预设周期内所述待监测工业区域的工业生产指数之后，还包括：
对所述预设周期内所述待监测工业区域的工业生产指数进行分析，确定出所述待监测工业区域的工业生产情况。
第二方面，本发明实施例提供一种监测工业生产指数的装置，包括：
确定单元，用于确定待监测工业区域的预设形状范围的坐标集合；
处理单元，用于根据所述坐标集合确定出所述坐标集合对应的预设周期内的遥感卫星多光谱数据；提取所述预设周期内的遥感卫星多光谱数据中多个预设波段的数据，基于所述多个预设波段的数据，得到所述待监测工业区域的图像；并确定出所述待监测工业区域的图像的二值化图；统计所述预设周期内所述待监测工业区域的图像的二值化图中像素符合预设像素条件的像素点的数量，得到所述预设周期内所述待监测工业区域的工业生产指数。
可选的，所述处理单元具体用于：
将所述待监测工业区域的图像进行色彩转换，得到所述待监测工业区域的图像的色彩空间数据；
依据设定提取范围对所述色彩空间数据进行颜色提取，将符合所述设定提取范围的像素点的像素设置为第一数值，将不符合所述设定提取范围的像素点的像素设置为第二数值。
可选的，所述处理单元具体用于：
对所述待监测工业区域的图像进行RGB颜色空间至HSV颜色空间的色彩转换，得到所述待监测工业区域的图像的HVS色彩空间数据。
可选的，所述确定单元具体用于：
获取待监测工业区域以及所述待监测工业区域的中心点坐标；
以所述待监测工业区域的中心点坐标为原点，外扩预设形状范围的区域，得到所述待监测工业区域的预设形状范围的坐标集合。
可选的，所述坐标集合中的坐标为经纬度坐标；
所述处理单元具体用于：
基于所述坐标集合中的经纬度坐标，从遥感卫星数据库中确定出预设周期内的所述坐标集合中的经纬度坐标对应的遥感卫星多光谱数据。
可选的，所述处理单元具体用于：
对所述多个预设波段的数据进行归一化处理；
将归一化处理后的所述多个预设波段的数据进行合并，得到所述待监测工业区域的图像。
可选的，所述处理单元具体用于：
针对所述多个预设波段中的任一预设波段，将第一差值和预设阈值的乘积与第二差值的比值确定为归一化处理后的所述预设波段的数据；其中，所述第一差值为所述预设波段的数据的值与所述预设波段的最小值的差值；所述第二差值为所述预设波段的最大值和最小值的差值。
可选的，所述处理单元还用于：
在得到所述预设周期内所述待监测工业区域的工业生产指数之后，对所述预设周期内所述待监测工业区域的工业生产指数进行分析，确定出所述待监测工业区域的工业生产情况。
第三方面，本发明提供一种计算设备，包括：
存储器，用于存储计算机程序；
处理器，用于调用所述存储器中存储的计算机程序，按照获得的程序执行上述第一方面所述的方法。
第四方面，本发明提供一种计算机可读存储介质，所述计算机可读存储介质存储有计算机可执行程序，所述计算机可执行程序用于使计算机执行上述第一方面所述的方法。
第五方面，本发明实施例还提供一种包含指令的计算机程序产品，所述计算机程序产品包括存储在计算机可读存储介质上的计算程序，所述计算机程序包括程序指令，当所述程序指令被计算机执行时，使所述计算机执行上述第一方面所述的方法。
 有益效果
上述技术方案中，通过确定出待监测工业区域的预设形状范围的坐标集合，来得到该坐标集合对应的遥感数据，然后再对遥感数据提取预设波段的数据后进行处理，得到该待监测工业区域的图像并进行二值化处理，统计像素预设像素条件的像素点的数量，就可以实现对工业生产指数的监测。由于监测过程中不涉及传感器设备的安装，因此可以降低工业生产监测的投入成本。而通过基于遥感数据的工业生产指数的监测，在数据处理过程中相比现有技术中的构建预测模型方式复杂度要低，能够实现在任意地区的任意范围的工业生产指数的监测。
 附图说明
为了更清楚地说明本发明实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简要介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域的普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为本发明实施例提供的一种系统架构的示意图；
图2为本发明实施例提供的一种监测工业生产指数的方法的流程示意图；
图3为本发明实施例提供的一种遥感数据的波段数据的示意图；
图4为本发明实施例提供的一种RGB图像的示意图；
图5为本发明实施例提供的一种二值化图像的示意图；
图6为本发明实施例提供的一种工业生产指数的示意图；
图7为本发明实施例提供的一种监测工业生产指数的装置的结构示意图；
图8为本发明实施例提供的一种计算设备的结构示意图。
 本发明的最佳实施方式
基于上述描述，图2示例性的示出了本发明实施例提供的一种监测工业生产指数的方法的流程，该流程可以由一种监测工业生产指数的装置执行。
如图2所示，该流程具体步骤包括：
步骤201，确定待监测工业区域的预设形状范围的坐标集合。
首先，需要获取待监测工业区域以及待监测工业区域的中心点坐标，然后以待监测工业区域的中心点坐标为原点，外扩预设形状范围的区域，就可以得到该待监测工业区域的预设形状范围的坐标集合。
其中，坐标集合中的坐标可以为经纬度坐标，也可以为地图坐标。例如，中心点坐标为中心点经纬度坐标，坐标集合为经纬度坐标集合。为了便于描述，下面举例时将以经纬度坐标为例进行描述。
该预设形状范围可以是矩形框范围、圆形框范围、梯形框范围等，本发明实施例对此不做具体限定。范围的大小可以依据经验设置。本发明实施例中的坐标集合可以是该预设形状范围内所有点的坐标，也可以是能够表示预设形状范围的坐标，或是该预设形状范围内的最小横纵坐标，例如最小经度坐标、最小维度坐标、最大经度坐标和最大维度坐标。
在具体实施过程中，首先要确定待监测工业区域，可以通过网络搜索和收集的方式，得到待监测工业区域N个。同时记录这N个待监测工业区域的中心点经纬度坐标(Lati,Loni)(i∈[1,N])。
再以该中心点经纬度坐标为原点，向外扩一个预设形状范围（如矩形框范围），就可以得到一个矩形框范围BOXi[最小经度,最小纬度,最大经度,最大纬度](i∈[1,N])。例如：BOXi=[Loni-Lon_bias,Lati-Lat_bias,Loni+Lon_bias,Lati+ Lat_ bias]。
步骤202，根据所述坐标集合确定出所述坐标集合对应的预设周期内的遥感卫星多光谱数据；
当在步骤201中得到坐标集合之后，就可以确定该坐标集合对应的遥感卫星多光谱数据，该预设周期可以依据经验设置，或者依据监测任务设置。该多个预设波段可以依据经验设置，例如该多个预设波段可以至少包括短波红外波段。该遥感卫星多光谱数据可以在遥感卫星数据库中获取，即基于上述坐标集合中的经纬度坐标，从遥感卫星多光谱数据中得到该预设周期内的坐标集合中的经纬度坐标对应的遥感卫星多光谱数据。遥感卫星多光谱数据可以包括多个波段，如图3所示，可以有红波段、绿波段、蓝波段、近红外波段、短波红外波段等。
遥感卫星多光谱数据每个波段的范围可以如图3所示，其中，Band 11和Band 12为两个短波红外波段。在本发明实施例中将以至少包括Band 12的多个波段为例进行数据提取并处理。
步骤203，提取所述预设周期内的遥感卫星多光谱数据中多个预设波段的数据，基于所述多个预设波段的数据，得到所述待监测工业区域的图像，并确定出所述待监测工业区域的图像的二值化图。
首先可以对多个预设波段的数据进行归一化处理，然后将归一化处理后的多个预设波段的数据进行合并，得到该待监测工业区域的图像。
在进行归一化处理时主要是使用百分比截断的方式将该多个预设波段的数据归一化到一个预设范围内。具体的，针对多个预设波段中的任一预设波段，可以将第一差值和预设阈值的乘积与第二差值的比值确定为归一化处理后的预设波段的数据。其中，所述第一差值为预设波段的数据的值与预设波段的最小值的差值。第二差值为预设波段的最大值和最小值的差值。其中预设波段的最大值和最小值是预设波段的波动范围中的最大值和最小值。该预设阈值可以依据经验设置。
例如，分别可以提取图3中的Band 12、Band 8A、Band 4的数据，运用百分比截断等方法，将Band 12、Band 8A、Band 4的数据分别归一化到0-255（预设范围）之间。
以Band 4的数据RAWband4为例，归一化得到RAWband4_s的计算方法为：
RAWband4_s = (RAWband4 - RAWband4_min)* 255 / (RAWband4_max - RAWband4_min)；
其中，RAWband4_min和RAWband4_max分别为RAWband4的波段的波动范围的最小值与最大值。
根据上述公式，依次计算可得到Band 4,Band 8A, Band 12这三个波段的归一化后的数值RAWband4_s，RAWband8a_s，RAWband12_s。
在得到各个预设波段的归一化后的数据后，就可以将该归一化处理后的多个预设波段的数据进行合并，合并的方式就是将多个预设波段的数据进行叠加处理，这样可以得到一个彩色的RGB图像，即为待监测工业区域的图像。
然后再对该待监测工业区域的图像进行转换处理，得到其对应的二值化图。在确定待监测工业区域的图像的二值化图的过程中，需要将待监测工业区域的图像进行色彩转换，得到待监测工业区域的图像的色彩空间数据。可以是对待监测工业区域的图像进行RGB颜色空间至HSV颜色空间的色彩转换，得到待监测工业区域的图像的HVS色彩空间数据。然后依据设定提取范围对色彩空间数据进行颜色提取，将符合设定提取范围的像素点的像素设置为第一数值，将不符合设定提取范围的像素点的像素设置为第二数值。该设定提取范围可以包括色调提取范围、饱和度提取范围和明度提取范围。该色调提取范围、饱和度提取范围和明度提取范围可以依据经验设置。该第一数值、第二设置可以依据经验设置。例如，该第一数值可以为1或255，该第二数值可以为0。或者是该第一数值为0，该第二数值为1或255。
举例来说，可以将上述实施例中得到的归一化后的数值RAWband4_s，RAWband8a_s，RAWband12_s进行波段合并操作。以某钢铁厂为例，将按照顺序[RAWband12_s, RAWband8a_s，RAWband4_s]合成该钢铁厂的RGB图像，如图4所示。图4中的黑色椭圆圈出的位置表示了钢铁厂的高温发热区域。
然后对图4所示的RGB图像进行RGB颜色空间-HSV颜色空间的色彩转换，得到该RGB图的HSV色彩空间数据。
分别设定H、S、V分量的提取范围为：       H∈[0,10]，S∈[140,255]，V∈[46,255]。对HSV色彩空间数据进行上述设定提取范围进行数据筛选。符合上述设定提取范围的数据元素设定成RGB[255,255,255]，不符合上述设定的范围的数据元素设定成RGB[0,0,0]。则得到上述RGB图像对应的黑白二值化图（黑色对应[0,0,0]，白色对应[255,255,255]）。其中图4对应的二值化图可以如图5所示。
步骤204，统计所述预设周期内所述待监测工业区域的图像的二值化图中像素符合预设像素条件的像素点的数量，得到所述预设周期内所述待监测工业区域的工业生产指数。
该预设像素条件可以依据经验设置，例如可以为像素大于预设像素阈值、像素位于预设像素阈值范围内等等。
统计图5所示的二值化图中的白色像素点总和SWIR-SMI（short-wave (length) infrared (band)- Stochastic Momentum Index，短波红外-随机动量指标）。该SWIR-SMI可以反映这片工业区域的工业生产总体情况。通过对预设周期内的待监测工业区域的工业生产指数进行分析，可以确定出待监测工业区域的工业生产情况。例如：以此方法，获取预设时期内整个钢铁行业的主要钢铁厂的短波红外图像，提取该预设时期内的各钢铁厂的SWIR-SMI，对该各钢铁厂的SWIR-SMI进行分析，就可以得到整个钢铁行业的工业生产情况。
如图6所示，黑色实线为按日期所提取的SWIR-SMI。黑色虚线为钢铁的工业增长值，也可以称为钢材生产指数。对钢材生产指数与SWIR-SMI进行相关性分析，相关系数R>设定值（如0.7），则有较强相关性，证明SWIR-SMI可以反映钢铁行业的钢材生产指数。
本发明实施例中，通过运用到了卫星遥感技术，理论上可以对一定周期内的全球任意地方的工业区域进行生产活动监测。运用到了多光谱数据处理与分析技术，可以从不同的特征层面对工业区生产活动进行分析与监测。运用到了图像处理技术，通过设定阈值的白点提取算法，可以提取有效工业生产区域的白点像素点值总和。
本发明实施例表明，通过确定待监测工业区域的预设形状范围的坐标集合，根据坐标集合确定出坐标集合对应的预设周期内的遥感卫星多光谱数据，提取预设周期内的遥感卫星多光谱数据中多个预设波段的数据，基于多个预设波段的数据，得到待监测工业区域的图像，并确定出待监测工业区域的图像的二值化图，统计预设周期内所述待监测工业区域的图像的二值化图中像素符合预设像素条件的像素点的数量，得到预设周期内待监测工业区域的工业生产指数。通过确定出待监测工业区域的预设形状范围的坐标集合，来得到该坐标集合对应的遥感数据，然后再对遥感数据提取预设波段的数据后进行处理，得到该待监测工业区域的图像并进行二值化处理，统计像素预设像素条件的像素点的数量，就可以实现对工业生产指数的监测。由于监测过程中不涉及传感器设备的安装，因此可以降低工业生产监测的投入成本。而通过基于遥感数据的工业生产指数的监测，在数据处理过程中相比现有技术中的构建预测模型方式复杂度要低，能够实现在任意地区的任意范围的工业生产指数的监测。
 本发明的实施方式
为了使本发明的目的、技术方案和优点更加清楚，下面将结合附图对本发明作进一步地详细描述，显然，所描述的实施例仅仅是本发明的一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其它实施例，都属于本发明保护的范围。
图1为本发明实施例提供的一种系统架构。如图1所示，该系统架构可以为服务器100，包括处理器110、通信接口120和存储器130。
其中，通信接口120用于与终端设备进行通信，收发该终端设备传输的信息，实现通信。
处理器110是服务器100的控制中心，利用各种接口和线路连接整个服务器100的各个部分，通过运行或执行存储在存储器130内的软件程序/或模块，以及调用存储在存储器130内的数据，执行服务器100的各种功能和处理数据。可选地，处理器110可以包括一个或多个处理单元。
存储器130可用于存储软件程序以及模块，处理器110通过运行存储在存储器130的软件程序以及模块，从而执行各种功能应用以及数据处理。存储器130可主要包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序等；存储数据区可存储根据业务处理所创建的数据等。此外，存储器130可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他易失性固态存储器件。
需要说明的是，上述图1所示的结构仅是一种示例，本发明实施例对此不做限定。
基于上述描述，图2示例性的示出了本发明实施例提供的一种监测工业生产指数的方法的流程，该流程可以由一种监测工业生产指数的装置执行。
如图2所示，该流程具体步骤包括：
步骤201，确定待监测工业区域的预设形状范围的坐标集合。
首先，需要获取待监测工业区域以及待监测工业区域的中心点坐标，然后以待监测工业区域的中心点坐标为原点，外扩预设形状范围的区域，就可以得到该待监测工业区域的预设形状范围的坐标集合。
其中，坐标集合中的坐标可以为经纬度坐标，也可以为地图坐标。例如，中心点坐标为中心点经纬度坐标，坐标集合为经纬度坐标集合。为了便于描述，下面举例时将以经纬度坐标为例进行描述。
该预设形状范围可以是矩形框范围、圆形框范围、梯形框范围等，本发明实施例对此不做具体限定。范围的大小可以依据经验设置。本发明实施例中的坐标集合可以是该预设形状范围内所有点的坐标，也可以是能够表示预设形状范围的坐标，或是该预设形状范围内的最小横纵坐标，例如最小经度坐标、最小维度坐标、最大经度坐标和最大维度坐标。
在具体实施过程中，首先要确定待监测工业区域，可以通过网络搜索和收集的方式，得到待监测工业区域N个。同时记录这N个待监测工业区域的中心点经纬度坐标(Lati,Loni)(i∈[1,N])。
再以该中心点经纬度坐标为原点，向外扩一个预设形状范围（如矩形框范围），就可以得到一个矩形框范围BOXi[最小经度,最小纬度,最大经度,最大纬度](i∈[1,N])。例如：BOXi=[Loni-Lon_bias,Lati-Lat_bias,Loni+Lon_bias,Lati+ Lat_ bias]。
步骤202，根据所述坐标集合确定出所述坐标集合对应的预设周期内的遥感卫星多光谱数据；
当在步骤201中得到坐标集合之后，就可以确定该坐标集合对应的遥感卫星多光谱数据，该预设周期可以依据经验设置，或者依据监测任务设置。该多个预设波段可以依据经验设置，例如该多个预设波段可以至少包括短波红外波段。该遥感卫星多光谱数据可以在遥感卫星数据库中获取，即基于上述坐标集合中的经纬度坐标，从遥感卫星多光谱数据中得到该预设周期内的坐标集合中的经纬度坐标对应的遥感卫星多光谱数据。遥感卫星多光谱数据可以包括多个波段，如图3所示，可以有红波段、绿波段、蓝波段、近红外波段、短波红外波段等。
遥感卫星多光谱数据每个波段的范围可以如图3所示，其中，Band 11和Band 12为两个短波红外波段。在本发明实施例中将以至少包括Band 12的多个波段为例进行数据提取并处理。
步骤203，提取所述预设周期内的遥感卫星多光谱数据中多个预设波段的数据，基于所述多个预设波段的数据，得到所述待监测工业区域的图像，并确定出所述待监测工业区域的图像的二值化图。
首先可以对多个预设波段的数据进行归一化处理，然后将归一化处理后的多个预设波段的数据进行合并，得到该待监测工业区域的图像。
在进行归一化处理时主要是使用百分比截断的方式将该多个预设波段的数据归一化到一个预设范围内。具体的，针对多个预设波段中的任一预设波段，可以将第一差值和预设阈值的乘积与第二差值的比值确定为归一化处理后的预设波段的数据。其中，所述第一差值为预设波段的数据的值与预设波段的最小值的差值。第二差值为预设波段的最大值和最小值的差值。其中预设波段的最大值和最小值是预设波段的波动范围中的最大值和最小值。该预设阈值可以依据经验设置。
例如，分别可以提取图3中的Band 12、Band 8A、Band 4的数据，运用百分比截断等方法，将Band 12、Band 8A、Band 4的数据分别归一化到0-255（预设范围）之间。
以Band 4的数据RAWband4为例，归一化得到RAWband4_s的计算方法为：
RAWband4_s = (RAWband4 - RAWband4_min)* 255 / (RAWband4_max - RAWband4_min)；
其中，RAWband4_min和RAWband4_max分别为RAWband4的波段的波动范围的最小值与最大值。
根据上述公式，依次计算可得到Band 4,Band 8A, Band 12这三个波段的归一化后的数值RAWband4_s，RAWband8a_s，RAWband12_s。
在得到各个预设波段的归一化后的数据后，就可以将该归一化处理后的多个预设波段的数据进行合并，合并的方式就是将多个预设波段的数据进行叠加处理，这样可以得到一个彩色的RGB图像，即为待监测工业区域的图像。
然后再对该待监测工业区域的图像进行转换处理，得到其对应的二值化图。在确定待监测工业区域的图像的二值化图的过程中，需要将待监测工业区域的图像进行色彩转换，得到待监测工业区域的图像的色彩空间数据。可以是对待监测工业区域的图像进行RGB颜色空间至HSV颜色空间的色彩转换，得到待监测工业区域的图像的HVS色彩空间数据。然后依据设定提取范围对色彩空间数据进行颜色提取，将符合设定提取范围的像素点的像素设置为第一数值，将不符合设定提取范围的像素点的像素设置为第二数值。该设定提取范围可以包括色调提取范围、饱和度提取范围和明度提取范围。该色调提取范围、饱和度提取范围和明度提取范围可以依据经验设置。该第一数值、第二设置可以依据经验设置。例如，该第一数值可以为1或255，该第二数值可以为0。或者是该第一数值为0，该第二数值为1或255。
举例来说，可以将上述实施例中得到的归一化后的数值RAWband4_s，RAWband8a_s，RAWband12_s进行波段合并操作。以某钢铁厂为例，将按照顺序[RAWband12_s, RAWband8a_s，RAWband4_s]合成该钢铁厂的RGB图像，如图4所示。图4中的黑色椭圆圈出的位置表示了钢铁厂的高温发热区域。
然后对图4所示的RGB图像进行RGB颜色空间-HSV颜色空间的色彩转换，得到该RGB图的HSV色彩空间数据。
分别设定H、S、V分量的提取范围为：       H∈[0,10]，S∈[140,255]，V∈[46,255]。对HSV色彩空间数据进行上述设定提取范围进行数据筛选。符合上述设定提取范围的数据元素设定成RGB[255,255,255]，不符合上述设定的范围的数据元素设定成RGB[0,0,0]。则得到上述RGB图像对应的黑白二值化图（黑色对应[0,0,0]，白色对应[255,255,255]）。其中图4对应的二值化图可以如图5所示。
步骤204，统计所述预设周期内所述待监测工业区域的图像的二值化图中像素符合预设像素条件的像素点的数量，得到所述预设周期内所述待监测工业区域的工业生产指数。
该预设像素条件可以依据经验设置，例如可以为像素大于预设像素阈值、像素位于预设像素阈值范围内等等。
统计图5所示的二值化图中的白色像素点总和SWIR-SMI（short-wave (length) infrared (band)- Stochastic Momentum Index，短波红外-随机动量指标）。该SWIR-SMI可以反映这片工业区域的工业生产总体情况。通过对预设周期内的待监测工业区域的工业生产指数进行分析，可以确定出待监测工业区域的工业生产情况。例如：以此方法，获取预设时期内整个钢铁行业的主要钢铁厂的短波红外图像，提取该预设时期内的各钢铁厂的SWIR-SMI，对该各钢铁厂的SWIR-SMI进行分析，就可以得到整个钢铁行业的工业生产情况。
如图6所示，黑色实线为按日期所提取的SWIR-SMI。黑色虚线为钢铁的工业增长值，也可以称为钢材生产指数。对钢材生产指数与SWIR-SMI进行相关性分析，相关系数R>设定值（如0.7），则有较强相关性，证明SWIR-SMI可以反映钢铁行业的钢材生产指数。
本发明实施例中，通过运用到了卫星遥感技术，理论上可以对一定周期内的全球任意地方的工业区域进行生产活动监测。运用到了多光谱数据处理与分析技术，可以从不同的特征层面对工业区生产活动进行分析与监测。运用到了图像处理技术，通过设定阈值的白点提取算法，可以提取有效工业生产区域的白点像素点值总和。
本发明实施例表明，通过确定待监测工业区域的预设形状范围的坐标集合，根据坐标集合确定出坐标集合对应的预设周期内的遥感卫星多光谱数据，提取预设周期内的遥感卫星多光谱数据中多个预设波段的数据，基于多个预设波段的数据，得到待监测工业区域的图像，并确定出待监测工业区域的图像的二值化图，统计预设周期内所述待监测工业区域的图像的二值化图中像素符合预设像素条件的像素点的数量，得到预设周期内待监测工业区域的工业生产指数。通过确定出待监测工业区域的预设形状范围的坐标集合，来得到该坐标集合对应的遥感数据，然后再对遥感数据提取预设波段的数据后进行处理，得到该待监测工业区域的图像并进行二值化处理，统计像素预设像素条件的像素点的数量，就可以实现对工业生产指数的监测。由于监测过程中不涉及传感器设备的安装，因此可以降低工业生产监测的投入成本。而通过基于遥感数据的工业生产指数的监测，在数据处理过程中相比现有技术中的构建预测模型方式复杂度要低，能够实现在任意地区的任意范围的工业生产指数的监测。
基于相同的技术构思，图7示例性的示出了本发明实施例提供的一种监测工业生产指数的装置的结构示意图，该装置可以执行监测工业生产指数的流程。
如图7所示，该装置具体包括：
确定单元701，用于确定待监测工业区域的预设形状范围的坐标集合；
处理单元702，用于根据所述坐标集合确定出所述坐标集合对应的预设周期内的遥感卫星多光谱数据；提取所述预设周期内的遥感卫星多光谱数据中多个预设波段的数据，基于所述多个预设波段的数据，得到所述待监测工业区域的图像；并确定出所述待监测工业区域的图像的二值化图；统计所述预设周期内所述待监测工业区域的图像的二值化图中像素符合预设像素条件的像素点的数量，得到所述预设周期内所述待监测工业区域的工业生产指数。
可选的，所述处理单元702具体用于：
将所述待监测工业区域的图像进行色彩转换，得到所述待监测工业区域的图像的色彩空间数据；
依据设定提取范围对所述色彩空间数据进行颜色提取，将符合所述设定提取范围的像素点的像素设置为第一数值，将不符合所述设定提取范围的像素点的像素设置为第二数值。
可选的，所述处理单元702具体用于：
对所述待监测工业区域的图像进行RGB颜色空间至HSV颜色空间的色彩转换，得到所述待监测工业区域的图像的HVS色彩空间数据。
可选的，所述确定单元701具体用于：
获取待监测工业区域以及所述待监测工业区域的中心点坐标；
以所述待监测工业区域的中心点坐标为原点，外扩预设形状范围的区域，得到所述待监测工业区域的预设形状范围的坐标集合。
可选的，所述坐标集合中的坐标为经纬度坐标；
所述处理单元702具体用于：
基于所述坐标集合中的经纬度坐标，从遥感卫星数据库中确定出预设周期内的所述坐标集合中的经纬度坐标对应的遥感卫星多光谱数据。
可选的，所述处理单元702具体用于：
对所述多个预设波段的数据进行归一化处理；
将归一化处理后的所述多个预设波段的数据进行合并，得到所述待监测工业区域的图像。
可选的，所述处理单元702具体用于：
针对所述多个预设波段中的任一预设波段，将第一差值和预设阈值的乘积与第二差值的比值确定为归一化处理后的所述预设波段的数据；其中，所述第一差值为所述预设波段的数据的值与所述预设波段的最小值的差值；所述第二差值为所述预设波段的最大值和最小值的差值。
可选的，所述处理单元702还用于：
在得到所述预设周期内所述待监测工业区域的工业生产指数之后，对所述预设周期内所述待监测工业区域的工业生产指数进行分析，确定出所述待监测工业区域的工业生产情况。
基于与上述图2所示的方法相同的构思，本申请还提供一种计算设备，如图8所示，该计算设备包括至少一个处理器820，用于实现本申请实施例提供的图2中任一方法。
计算设备800还可以包括至少一个存储器830，用于存储程序指令和/或数据。存储器830和处理器820耦合。本申请实施例中的耦合是装置、单元或模块之间的间接耦合或通信连接，可以是电性，机械或其它的形式，用于装置、单元或模块之间的信息交互。处理器820可能和存储器830协同操作。处理器820可能执行存储器830中存储的程序指令。所述至少一个存储器中的至少一个可以包括于处理器中。
在实现过程中，上述方法的各步骤可以通过处理器中的硬件的集成逻辑电路或者软件形式的指令完成。结合本申请实施例所公开的方法的步骤可以直接体现为硬件处理器执行完成，或者用处理器中的硬件及软件模块组合执行完成。软件模块可以位于随机存储器，闪存、只读存储器，可编程只读存储器或者电可擦写可编程存储器、寄存器等本领域成熟的存储介质中。该存储介质位于存储器，处理器读取存储器中的信息，结合其硬件完成上述方法的步骤。为避免重复，这里不再详细描述。
应注意，本申请实施例中的处理器可以是一种集成电路芯片，具有信号的处理能力。在实现过程中，上述方法实施例的各步骤可以通过处理器中的硬件的集成逻辑电路或者软件形式的指令完成。上述的处理器可以是通用处理器、数字信号处理电路（digital signal processor，DSP）、专用集成芯片（application specific integrated circuit，ASIC）、现场可编程门阵列（field programmable gate array，FPGA）或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。可以实现或者执行本申请实施例中的公开的各方法、步骤及逻辑框图。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。结合本申请实施例所公开的方法的步骤可以直接体现为硬件译码处理器执行完成，或者用译码处理器中的硬件及软件模块组合执行完成。软件模块可以位于随机存储器，闪存、只读存储器，可编程只读存储器或者电可擦写可编程存储器、寄存器等本领域成熟的存储介质中。该存储介质位于存储器，处理器读取存储器中的信息，结合其硬件完成上述方法的步骤。
可以理解，本申请实施例中的存储器可以是易失性存储器或非易失性存储器，或可包括易失性和非易失性存储器两者。其中，非易失性存储器可以是只读存储器（read-only memory，ROM）、可编程只读存储器（programmable ROM，PROM）、可擦除可编程只读存储器（erasable PROM，EPROM）、电可擦除可编程只读存储器（electrically EPROM，EEPROM）或闪存。易失性存储器可以是随机存取存储器（random access memory，RAM），其用作外部高速缓存。通过示例性但不是限制性说明，许多形式的RAM可用，例如静态随机存取存储器（static RAM，SRAM）、动态随机存取存储器（dynamic RAM，DRAM）、同步动态随机存取存储器（synchronous DRAM，SDRAM）、双倍数据速率同步动态随机存取存储器（double data rate SDRAM，DDR SDRAM）、增强型同步动态随机存取存储器（enhanced SDRAM，ESDRAM）、同步连接动态随机存取存储器（synchlink DRAM，SLDRAM）和直接内存总线随机存取存储器（direct rambus RAM，DR RAM）。应注意，本文描述的系统和方法的存储器旨在包括但不限于这些和任意其它适合类型的存储器。
计算设备800还可以包括通信接口810，用于通过传输介质和其它设备进行通信，从而用于计算设备800中的装置可以和其它设备进行通信。在本申请实施例中，通信接口可以是收发器、电路、总线、模块或其它类型的通信接口。在本申请实施例中，通信接口为收发器时，收发器可以包括独立的接收器、独立的发射器；也可以集成收发功能的收发器、或者是接口电路。
计算设备800还可以包括通信线路840。其中，通信接口810、处理器820以及存储器830可以通过通信线路840相互连接；通信线路840可以是外设部件互连标准（peripheral component interconnect，简称PCI）总线或扩展工业标准结构（extended industry standard architecture，简称EISA）总线等。所述通信线路840可以分为地址总线、数据总线、控制总线等。为便于表示，图8中仅用一条粗线表示，但并不表示仅有一根总线或一种类型的总线。
基于相同的技术构思，本发明实施例提供一种计算设备，包括：
存储器，用于存储计算机程序；
处理器，用于调用所述存储器中存储的计算机程序，按照获得的程序执行上述监测工业生产指数的方法。
基于相同的技术构思，本发明实施例提供一种计算机可读存储介质，所述计算机可读存储介质存储有计算机可执行程序，所述计算机可执行程序用于使计算机执行上述监测工业生产指数的方法。
基于相同的技术构思，本发明实施例提供一种计算机程序产品，所述计算机程序产品包括存储在计算机可读存储介质上的计算程序，所述计算机程序包括程序指令，当所述程序指令被计算机执行时，使所述计算机执行上述监测工业生产指数的方法。
本领域内的技术人员应明白，本发明的实施例可提供为方法、系统、或计算机程序产品。因此，本发明可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本发明可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质（包括但不限于磁盘存储器、CD-ROM、光学存储器等）上实施的计算机程序产品的形式。
本发明是参照根据本发明的方法、设备（系统）、和计算机程序产品的流程图和／或方框图来描述的。应理解可由计算机程序指令实现流程图和／或方框图中的每一流程和／或方框、以及流程图和／或方框图中的流程和／或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和／或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和／或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和／或方框图一个方框或多个方框中指定的功能的步骤。
尽管已描述了本发明的优选实施例，但本领域内的技术人员一旦得知了基本创造性概念，则可对这些实施例作出另外的变更和修改。所以，所附权利要求意欲解释为包括优选实施例以及落入本发明范围的所有变更和修改。
显然，本领域的技术人员可以对本发明进行各种改动和变型而不脱离本发明的精神和范围。这样，倘若本发明的这些修改和变型属于本申请权利要求及其等同技术的范围之内，则本发明也意图包含这些改动和变型在内。
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Claims (19)

  1. 一种监测工业生产指数的方法，其特征在于，包括：
    确定待监测工业区域的预设形状范围的坐标集合；
    根据所述坐标集合确定出所述坐标集合对应的预设周期内的遥感卫星多光谱数据；
    提取所述预设周期内的遥感卫星多光谱数据中多个预设波段的数据，基于所述多个预设波段的数据，得到所述待监测工业区域的图像，并确定出所述待监测工业区域的图像的二值化图；
    统计所述预设周期内所述待监测工业区域的图像的二值化图中像素符合预设像素条件的像素点的数量，得到所述预设周期内所述待监测工业区域的工业生产指数。
  2. 如权利要求1所述的方法，其特征在于，所述确定出所述待监测工业区域的图像的二值化图，包括：
    将所述待监测工业区域的图像进行色彩转换，得到所述待监测工业区域的图像的色彩空间数据；
    依据设定提取范围对所述色彩空间数据进行颜色提取，将符合所述设定提取范围的像素点的像素设置为第一数值，将不符合所述设定提取范围的像素点的像素设置为第二数值。
  3. 如权利要求2所述的方法，其特征在于，所述将所述待监测工业区域的图像进行色彩转换，得到所述待监测工业区域的图像的色彩空间数据，包括：
    对所述待监测工业区域的图像进行红-绿-蓝RGB颜色空间至色调-饱和度-明度HSV颜色空间的色彩转换，得到所述待监测工业区域的图像的HVS色彩空间数据。
  4. 如权利要求1所述的方法，其特征在于，所述确定待监测工业区域的预设形状范围的坐标集合，包括：
    获取待监测工业区域以及所述待监测工业区域的中心点坐标；
    以所述待监测工业区域的中心点坐标为原点，外扩预设形状范围的区域，得到所述待监测工业区域的预设形状范围的坐标集合。
  5. 如权利要求1所述的方法，其特征在于，所述坐标集合中的坐标为经纬度坐标；
    所述根据所述坐标集合确定出所述坐标集合对应的预设周期内的遥感卫星多光谱数据，包括：
    基于所述坐标集合中的经纬度坐标，从遥感卫星数据库中确定出预设周期内的所述坐标集合中的经纬度坐标对应的遥感卫星多光谱数据。
  6. 如权利要求1所述的方法，其特征在于，所述基于所述多个预设波段的数据，得到所述待监测工业区域的图像，包括：
    对所述多个预设波段的数据进行归一化处理；
    将归一化处理后的所述多个预设波段的数据进行合并，得到所述待监测工业区域的图像。
  7. 如权利要求6所述的方法，其特征在于，所述对所述多个预设波段的数据进行归一化处理，包括：
    针对所述多个预设波段中的任一预设波段，将第一差值和预设阈值的乘积与第二差值的比值确定为归一化处理后的所述预设波段的数据；其中，所述第一差值为所述预设波段的数据的值与所述预设波段的最小值的差值；所述第二差值为所述预设波段的最大值和最小值的差值。
  8. 如权利要求1至7任一项所述的方法，其特征在于，在得到所述预设周期内所述待监测工业区域的工业生产指数之后，还包括：
    对所述预设周期内所述待监测工业区域的工业生产指数进行分析，确定出所述待监测工业区域的工业生产情况。
  9. 一种监测工业生产指数的装置，其特征在于，包括：
    确定单元，用于确定待监测工业区域的预设形状范围的坐标集合；
    处理单元，用于根据所述坐标集合确定出所述坐标集合对应的预设周期内的遥感卫星多光谱数据；提取所述预设周期内的遥感卫星多光谱数据中多个预设波段的数据，基于所述多个预设波段的数据，得到所述待监测工业区域的图像；并确定出所述待监测工业区域的图像的二值化图；统计所述预设周期内所述待监测工业区域的图像的二值化图中像素符合预设像素条件的像素点的数量，得到所述预设周期内所述待监测工业区域的工业生产指数。
  10. 如权利要求9所述的装置，其特征在于，所述处理单元具体用于：
    将所述待监测工业区域的图像进行色彩转换，得到所述待监测工业区域的图像的色彩空间数据；
    依据设定提取范围对所述色彩空间数据进行颜色提取，将符合所述设定提取范围的像素点的像素设置为第一数值，将不符合所述设定提取范围的像素点的像素设置为第二数值。
  11. 如权利要求10所述的装置，其特征在于，所述处理单元具体用于：
    对所述待监测工业区域的图像进行RGB颜色空间至HSV颜色空间的色彩转换，得到所述待监测工业区域的图像的HVS色彩空间数据。
  12. 如权利要求9所述的装置，其特征在于，所述确定单元具体用于：
    获取待监测工业区域以及所述待监测工业区域的中心点坐标；
    以所述待监测工业区域的中心点坐标为原点，外扩预设形状范围的区域，得到所述待监测工业区域的预设形状范围的坐标集合。
  13. 如权利要求9所述的装置，其特征在于，所述坐标集合中的坐标为经纬度坐标；
    所述处理单元具体用于：
    基于所述坐标集合中的经纬度坐标，从遥感卫星数据库中确定出预设周期内的所述坐标集合中的经纬度坐标对应的遥感卫星多光谱数据。
  14. 如权利要求9所述的装置，其特征在于，所述处理单元具体用于：
    对所述多个预设波段的数据进行归一化处理；
    将归一化处理后的所述多个预设波段的数据进行合并，得到所述待监测工业区域的图像。
  15. 如权利要求14所述的装置，其特征在于，所述处理单元具体用于：
    针对所述多个预设波段中的任一预设波段，将第一差值和预设阈值的乘积与第二差值的比值确定为归一化处理后的所述预设波段的数据；其中，所述第一差值为所述预设波段的数据的值与所述预设波段的最小值的差值；所述第二差值为所述预设波段的最大值和最小值的差值。
  16. 如权利要求9至15任一项所述的装置，其特征在于，所述处理单元还用于：
    在得到所述预设周期内所述待监测工业区域的工业生产指数之后，对所述预设周期内所述待监测工业区域的工业生产指数进行分析，确定出所述待监测工业区域的工业生产情况。
  17. 一种计算设备，其特征在于，包括：
    存储器，用于存储计算机程序；
    处理器，用于调用所述存储器中存储的计算机程序，按照获得的程序执行权利要求1至8任一项所述的方法。
  18. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质存储有计算机可执行程序，所述计算机可执行程序用于使计算机执行权利要求1至8任一项所述的方法。
  19. 一种计算机程序产品，其特征在于，所述计算机程序产品包括存储在计算机可读存储介质上的计算程序，所述计算机程序包括程序指令，当所述程序指令被计算机执行时，使所述计算机执行权利要求1至8任一项所述方法。
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